This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
1-10 Engineers Focus: Rapid product delivery to find mythical product market fit Architecture: a well designed 12-factor MVC app running securely on Heroku or Google App Engine, or any other PaaS platform. I suggest using a proven MVC framework like Rails, Django, Express, Sails, etc. Architecture: Small microservices targeting new features.
Your first priority: finding product-market fit. In any startup, finding the best product-market fit comes from getting feedback from customers. Over 20 years of finding product-market fit. Speedy development = quicker discovery of product-market fit. I didn’t waste any time and I got valuable signals about my market.
With preconfigured components and platform configurability, FAST enables carriers to reduce product time-to-market by 75% and launch new offerings in as little as 2 months. Verisk compared using Amazon OpenSearch Serverless with several embedding approaches and Amazon Kendra, and saw better retrieval results with Amazon Kendra.
Amazon OpenSearch Serverless is used as a vector database to store the embeddings of text chunks extracted from quality report PDFs and image descriptions. It also supports OEMs in enhancing customer satisfaction, improving features, and driving sales growth in a competitive market.
We organize all of the trending information in your field so you don't have to. Join 49,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content